MLOps Engineer (DevOps/SRE)

This job was posted over 30 days ago, so it has most likely closed. See newer jobs like it.

Written by AI from the job title and company, not by Jobs via Dice, so some details may not match this job. The employer's own page has the real description.

Overview

The MLOps Engineer (DevOps/SRE) is responsible for bridging the gap between machine learning (ML) and operations by enabling the continuous integration and deployment of ML models. This role involves collaborating closely with data scientists and software engineers to create robust infrastructure, automate workflows, and streamline the end-to-end ML lifecycle. The ideal candidate will leverage their expertise in DevOps practices to ensure reliable, scalable, and maintainable ML systems, ultimately enhancing the performance and efficiency of the organization’s AI initiatives.

Job Responsibilities

• Design, build, and maintain scalable ML infrastructure and pipelines
• Automate deployment processes using CI/CD practices
• Monitor and troubleshoot ML model performance and system health
• Collaborate with data scientists to refine model development workflows
• Implement tools and frameworks for model versioning and experimentation
• Ensure high availability and reliability of ML applications
• Document infrastructure, processes, and best practices for MLOps
• Support production deployments and manage operational risks

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field
• 3+ years of experience in MLOps, DevOps, or Site Reliability Engineering
• Proficiency in programming languages such as Python or Go
• Strong understanding of machine learning algorithms and data structures
• Experience with containerization technologies such as Docker and Kubernetes
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP)
• Knowledge of CI/CD tools like Jenkins, GitLab CI, or CircleCI
• Experience with monitoring tools (e.g., Prometheus, Grafana) and logging frameworks

Technologies & Tools

The MLOps Engineer will be working with a variety of technologies and tools, including but not limited to Docker, Kubernetes, Airflow, TensorFlow, PyTorch, Jenkins, Git, AWS, and Google Cloud Platform. Knowledge of monitoring systems like Prometheus or Grafana will also be essential to ensure effective oversight of ML operations. The role demands a practical understanding of cloud services and an ability to utilize ML libraries and frameworks for optimal automation and performance of machine learning models.

Ideal Candidates

Ideal candidates for the MLOps Engineer position are proactive problem solvers with a strong analytical mindset. They possess excellent communication skills to facilitate collaboration between cross-functional teams and have a passion for leveraging technology to improve operational efficiencies. A keen eye for detail, adaptability to evolving technologies, and a commitment to continuous learning are traits that will enable success in this role. Moreover, candidates should demonstrate a solid understanding of both DevOps and data science principles, as well as a drive to foster innovation within the organization.